# Optimization and joint modeling for peptide detection by tandem mass spectrometry

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2020 · $315,830

## Abstract

Project Summary/Abstract
Proteins are the primary functional molecules in living cells, and tandem mass spectrometry provides the most
efficient means of studying proteins in a high-throughput fashion. The proposal aims to use state-of-the-art
methods from the fields of machine learning, statistics, and natural language processing to improve our ability to
make sense of large tandem mass spectrometry data sets. Our project will focus on three key problems in the
analysis of such data:
1. facilitating the use of previously annotated spectra to improve our ability to annotate new spectra by creating
 a hybrid search scheme that compares an observed spectrum to a database comprised of theoretical spectra
 and previously annotated spectra,
2. enabling the efficient and accurate detection of peptides containing post-translational modifications and
 sequence variants, and
3. detecting sets of peptide species that are co-fragmented in the mass spectrometer and hence give rise to
 complex, mixture spectra.
Each of these aims will improve the ability of mass spectrometrists to efficiently and accurately identify and quantify
proteins in complex mixtures. To increase the impact of our work, we will continue to make all of our tools available
as free software.

## Key facts

- **NIH application ID:** 9856476
- **Project number:** 5R01GM121818-04
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** William Stafford Noble
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $315,830
- **Award type:** 5
- **Project period:** 2017-02-01 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9856476

## Citation

> US National Institutes of Health, RePORTER application 9856476, Optimization and joint modeling for peptide detection by tandem mass spectrometry (5R01GM121818-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9856476. Licensed CC0.

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